Maximum likelihood joint estimation of channel and noise for robust speech recognition

An EM algorithm is formulated in the DFT domain for joint estimation of parameters of distortion channel and additive noise from online degraded speech, and the posterior estimates of short-time speech power spectra are obtained at the convergence of the EM algorithm. Any speech features derivable from power spectra can then be approximately estimated by minimum mean-squared error estimation. Experiments were performed on speaker-independent continuous speech recognition using as features the perceptually based linear prediction cepstral coefficients, energy, and temporal regression coefficients. Speech data were taken from the TIMIT database and were degraded by a distortion channel and colored noise at various SNR levels. Experimental results indicate that the proposed technique leads to convergent identification of channel and noise and significantly improved recognition accuracy.